Microbial seedbanks and their evolutionary implications

Will Shoemaker

Background on dormancy

  • Resources are finite

  • Microbes respond by enterint a reversible state of low metabolic activity

  • Ecological implications well-characterized(Jones & Lennon, 2010; Lennon & Jones, 2011)

  • So are molecular & physiological mechanisms (Sturm & Dworkin, 2015)

In [3]:
from IPython.display import display, Image
display(Image(filename='Figs/DormRes.png'))
In [3]:
display(Image(filename='Figs/Migration_model2.png'))
  • Formation of dormant pool = population stratification
  • Impacts evolutionary inference
  • Effect detectable on molecular level
  • Similar to other demography-altering processes
In [5]:
display(Image(filename='Figs/CoalExample.jpg'))
In [6]:
display(Image(filename='Figs/DormCoal.png'))

Dormancy affects the geneology

  • But how?

Dormancy and bean bag genetics

In [20]:
display(Image(filename='Figs/BeanBag.png'))
In [4]:
display(Image(filename='Figs/WFillustration.png'))
In [11]:
display(Image(filename='Figs/PopGenMut.png'))

WF-model without resampling

  • Crude, simple, to the point
  • divergence = $ N_{e} \mu * 1/N_{e} = \mu$
    • Assuming neutrality
    • $N_{e}*\mu$ mutations across the entire population
    • Each mutation has a $1/N_{e}$ chance of fixing.
    • Rate at which mutations fix is $\mu$ (Kimua 1968)
In [12]:
display(Image(filename='Figs/WF.png'))

Break the WF-model

In [13]:
display(Image(filename='Figs/WFdorm.png'))

Little data on microbial seed-banks & evolution

Recent surge of theoretical work

  • Founded in coalescent theory (Blath et al., 2014; Kaj et al., 2001)

    • Wright-Fisher model with a seed-bank component
  • Useful framework to examine neutral evolution of a microbial dormant population

    • $D_{FL}$
    • Normalized observed minus expected genetic variation
In [7]:
display(Image(filename='Figs/DFL1.png'))
In [8]:
display(Image(filename='Figs/DFL2.png'))
  • Positive $D_{FL}$

  • Naive view:

    • Selection maintains variation
    • Recent population bottleneck
    • Selective sweep
$$D_{FL} = \frac{S-a(n)\xi_{i} }{\sqrt{u_{n}S+v_{n}S^{2}}}$$

Beyond the coalescent...

  • Start with population of all dormant indiduals
  • #### Focus on core characteristics
    • No, or very slow, turnover
    • Long survivorship
    • Still has a mutation rate
      • No replication-associated mutation
      • Deconstructing the mutation rate
  • Controversy on stress-induced mutation in dormant pops

  • Seed-banks proposed as source of genetic variation in plants and microbes (Levin 1990; Sogin et al., 2006; González-Casanova, 2004)

  • Dormancy considered trait that’s under selection (Dalling et al., 2011)

In [3]:
display(Image(filename='Figs/MAdist.png'))

$\mu$ and dormancy

  • Old hyp: Environmental stress as selective force on $\mu$ (Foster 1998 Corzett et al. 2013)
    • Doesn’t change site-specific $\mu$
    • Doesn’t change beneficial $\mu$
  • New hyp = Higher $\mu$ of dormant pops. as an obvious outcome
    • Mutation rate is a phenotype
    • Drift-barrier hypothesis (Sung et al., 2012)
      • Population genetics > biophysics
    • Limit on efficiency of selection (i.e. $\left | N_{e} * s \right | > 1.0$)
    • Difficult for selection to maintain unused pathways

Mutation-drift equilibrium

Expected neutral site heterozygosity

In [17]:
display(Image(filename='Figs/DriftMutEquilbJustAct.png'))
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display(Image(filename='Figs/DriftMutEquilb1.png'))
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display(Image(filename='Figs/DriftMutEquilb2.png'))
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display(Image(filename='Figs/DriftMutEquilb3.png'))

But we don't infer $\theta$, we estimate it.

ATTTACGG
ATTTACAG     $\hat{\theta _{W}} = \frac{K}{a_{n}} = \frac{K}{\sum_{1}^{n-1}\frac{1}{i}} = \frac{3}{\frac{1}{1}+\frac{1}{2}+\frac{1}{3}+\frac{1}{4}} \approx 1.44$
ATCTACGG
ATTTACGG
ATTTTCGG

So we must infer $N_{e}$

  • $N_{e} = \frac{\theta}{2\mu}$
  • For $N_{e_{D}} > N_{e_{A}} = N_{e}$
    • Low $\mu$
      • Or high repair under stress
    • High $\theta$
      • Migration, MRCA pushed back
      • Recent bottleneck
  • For $N_{e} = N_{e_{A}} > N_{e_{D}}$
    • High $\mu$
      • Population genetic limits to selection on repair fidelity
    • Low $\theta$
      • Recent population expansion

What does all this tell us?

  • Simply having a portion of the population not reproducng and persisting (i.e. dormancy) should have an effect on the population's genetic makeup, with or without $\mu$
  • Difficult, but not impossible to infer

What's next?

  • Refine model
    • Two-way IBM model of migration
  • Model selection
  • Model effects of $\mu_{A}$ and $\mu_{D}$
  • Model effects of different rates of migration between dormant and active subpopulations
  • Compare to existing work
    • Refine model
$$\mathbf{Data} \leftrightarrow \mathbf{Theory}$$

Essential to tie in experimental work

  • Core questions about basic population dynamics that haven't been answered
  • Need for basic, controlled experiments to determine evolutionary impact of dormancy
  • I'm not good enough at math to only do theory

Take flask picture

Demographic +/-'s with batch culture

:)

  • If a seed bank forms, it stays there

:(

  • Different rates of resuscitation/ dormancy (i.e. migration) during growth kinetics
  • Have to track both population size and migration rate
    • Better to start with seed-bank as only demographic factor
  • Risk loosing low-frequency alleles during transfers

Solution

Ensuring steady-state dynamics

  • Chemostats
  • Adjust dilution rate to allow for a constant total population size and different dormant sub-population sizes across treatments
  • Problem: I don't know anything about chemostats

Monitoring subpopulation sizes

  • Flow cytometry
    • DNA associated dye
    • Respiration associated dye
    • Membrane permeabilization associated dye
In [18]:
display(Image(filename='Figs/FlowExample.jpg'))

What organism?

guess...

In [18]:
display(Image(filename='Figs/IMG_0530.jpg'))

Janthinobacterium sp. KBS0711

  • Exhibits population dynamics associated with organisms capable of entering long-term dormancy
    • Does so in the wild (Alonso-Sáez etal., 2014)
  • Genes associated with the ability to contend with a stressful state
    • PhoR-PhoB
  • MA currently underway
  • Awesome pub (Shoemaker et al., 2015)